Performance Evaluation of Time-Multiplexed and Data-Dependent Superimposed Training Based Transmission With Practical Power Amplifier Model
The increase in the Peak-to-Average Power Ratio (PAPR) is a well known but not sufficiently addressed problem with Data-Dependent Superimposed Training (DDST) based approaches for channel estimation and synchronization in digital communication links. In this paper, the authors concentrate on the PAPR analysis with DDST and on the spectral regrowth with a nonlinear amplifier. In addition, a novel Gaussian distribution model based on the multinomial distribution for the cyclic mean component is presented. They propose the use of a symbol level amplitude limiter in the transmitter together with a modified channel estimator and iterative data bit estimator in the receiver. They show that this setup efficiently reduces the regrowth with the DDST.